To date, scaling up the size and depth of DNN quickly becomes prohibitive due to the emergence of gradient vanishing [1]} and overfitting issues. Our two-stage super-sub framework provides a new and efficient way of scaling up a DNN beyond the current limitations. Our framework subdivides the overall classification problem domain into separate smaller sub-domains that are semantically grouped. Each image goes first through a high-level classification by a Superclass Network which then routes it to the appropriate Subclass Network specialized for the sub-domain fine-grained classification. In our approach, each sub-domain is only a subset of the original overall classification problem. Hence, each can be expressed through a smaller model with less parameters and therefore less overfitting when compared to using a larger model to tackle directly the original problem. As previously discussed, naively increasing the size and depth within one DNN is not scalable because of gradient vanishing. Here, our model addresses this by increasing the number of parameters "laterally" into several separate network modules (sub-domains) which naturally confers more robustness to gradient vanishing and allows a much greater potential for upward scalability.
